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Industry experts, including Logical Construct’s Paul Kelly, provide their thoughts on how a better data strategy is key to streamlining collateral management processes.

https://www.reformis.com/insight-the-isda-common-domain-model-cdm-part-3/

We also recommend the two previous articles in the series – the first provides an overview of the CDM, and the second focuses on Digital Regulatory Reporting.

Photo by Joshua Sortino on Unsplash

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In his recently published paper The Coming AI Hackers, Security guru Bruce Schneier takes a look at how artificial intelligence will be applied to hacking. This means hacking everything from IT systems to economic, social and political systems – and how AI systems will first be used to hack us, and then will themselves become the hackers. The full paper is around 50 pages long but is very readable (and well worth the time). Given the work going on at the moment to develop standards that could lead to ‘smart’ derivatives contracts (containing executable code), some of the examples he gives in the paper seem relevant.

We’re asked to imagine feeding national or even global tax laws into an AI system. The tax law consists of formulas for calculation of the tax due, but there is often ambiguity and this makes codification problematic – giving us a basic level of defence against the AI systems (and, as he comments in the paper, guaranteeing that “there will be full employment for tax lawyers for the foreseeable future”!). 

However, when applied to areas where the rules are “designed to be algorithmically tractable” (he gives the example of the financial system), providing the AI system with all the relevant information and giving it a goal of “maximum profit legally” will result in new hacks, some of which are likely to be beyond human comprehension – we won’t even realise they are happening.

This might sound like science fiction, but with rapidly advancing technology it could become a reality “within the next decade”. The regulators and policy makers responsible for mitigating systemic risk to the financial system certainly won’t be short of work either.

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I’ve spent a large part of the past 20 years working on systems that need information from the CSA and other collateral documents. Enhancements to collateral, optimisation and risk systems depend on access to the granular data that is defined in these contracts. Getting it off the page and into a database has been a repeated challenge, along with mapping it to a variety of vendor and proprietary data formats. The data mapping exercise is a time consuming and unappetising prospect, but unavoidable in the absence of an industry-standard model.

In the early days the focus was on the precise data points needed by each target system – the same terms keyed-in multiple times in different ways. If the actual terms specified in the contract didn’t quite fit, they were simplified so that they did. With a complex rating or NAV based threshold you might just capture the current value; for a particularly dense or obscure collateral definition it was a case of ‘best efforts’.

Following the financial crisis this was less acceptable, but the problem with trying to define a sufficiently flexible data model up front becomes apparent when used across decades-worth of real-life contracts. It’s not just the ISDA CSA you have to deal with, you need to cater for Repo and Securities Lending agreements along with domestic equivalents such as the Spanish, French and German agreements. A contract-centric view is essential (data quality depends on being able to link back to the source) as is a single representation for a common concept across all those agreement types.

We have evolved our model over tens of thousands of documents and multiple projects and clients, and there is enormous value in having a database of all your contract data in a structured format, linking directly to the original document. Having said that, it’s only when the collateral, risk and trading systems consume it that the full value is realised. The ISDA CDM has the potential to be the lingua franca, enabling interoperability between the legal contracts and the business systems.

By making the CDM freely available, ISDA is actively encouraging widespread adoption. Extending the CDM to cover the full suite of ISDA Credit Support documents (legacy, IM, VM) is a positive development, as is recent news of a collaboration with ISLA to add components for securities finance transactions. The unwelcome need for data transformation projects has been a blight on innovation in this area, but the end may be in sight.

Paul.

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Covid-19 is the trigger for the latest trawl through the contracts, in anticipation of the impact the global pandemic will have on the markets. There aren’t really any surprises in the set of provisions that are of interest across a range of Master Agreements (such as the ISDA, GMRA and GMSLA):

• Termination Events and Events of Default – including granular information on grace periods and fallback provisions
• Additional Termination Events – particularly those linked to market volatility such as ratings downgrade triggers and net asset value triggers
• Delivery of Notices – which may be far from straightforward in a lockdown, especially if the master agreement doesn’t permit email as a valid method of service
• Cross Default – breaches in other agreements triggered by market disruption and volatility

These same provisions (and the same contracts) will almost certainly have been the focus of previous fire-drills as well as ad-hoc business-critical queries. Finding the contracts and subsequent amendments and pulling the data into a report is a time-consuming and error-prone task, and certainly not one that you want to repeat every couple of years. Sure, due-diligence tools can help speed up a review by locating relevant clauses – but in a pressure situation what you really want is granular, trusted data that you can instantly make use of.

Logical Construct’s Lyncs platform breaks the cycle of contract data abstraction, enabling firms to rapidly react to events and to be pro-active in preparing for the unexpected.

Follow the links to the product overview and case study for more information about Contract Data Management with Lyncs.

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Financial institutions are being pressed to keep pace with a myriad of regulations including uncleared margin reform, the Packaged Retail Investment and Insurance-Based Products (PRIIPs) regulation, minimum record-keeping requirements under the Bank Recovery and Resolution Directive (BRRD) and Dodd-Frank. As a result, there is a growing need to better handle contract data and access the contractual terms buried in agreements. By joining forces, Thomson Reuters and Logical Construct are able to provide the technology and processes to help financial institutions build a holistic view of such data.

The collaboration between the two organizations combines advanced data extraction technology from Logical Construct, with the scalable alternative legal managed services offered by Thomson Reuters. The integrated offering converts paper and images to searchable text, delivers pre-built and customizable data models to extract key legal and economic data, and provides dashboards, workflow and filters to identify key agreements and their respective data points. Thomson Reuters Legal Managed Services manages the technology and related processes to ensure a rigorous level of quality control and assurance.

The combination will enable global financial institutions to turn the unstructured data that resides within their agreements, into structured data that can be analyzed in seconds and connected to internal and external systems, such as risk, legal, pricing and collateral management systems through the use of APIs.

In addition to delivering data that is accurate, searchable and auditable, the integrated service offering is flexible and highly adaptable to a vast array of relationship and transactional level documentation such as ISDA Master Agreements, Credit Support Annexes, GMRAs, MRAs, prospectuses, Master Confirmation Agreements, Pricing Supplements and many more.

“Thomson Reuters strives to address its clients’ ever-changing challenges. Through this collaboration with Logical Construct, we help banks derive intelligence from their vital contract data with a single vendor full-service solution, that combines our legal expertise and deep domain knowledge from working with many of the world’s largest global banks with leading-edge technology that is purpose-built to handle complex documentation needs,” said Eric Laughlin, Managing Director, Thomson Reuters Legal Managed Services.

“Many financial services companies still face the daunting challenge of accessing data that lies buried in image-based scans of contractual documents,” said Luke Trigg, Managing Director, Logical Construct. “We are excited Thomson Reuters is collaborating with us and leveraging our platform to round out its industry-leading legal managed services for the benefit of global banks and other financial institutions.”

Thomson Reuters Legal Managed Services provides alternative legal services including financial trade documentation, regulatory change management, managed discovery, document review and analysis, and contract management. Logical Construct’s Lyncs platform uses a combination of information extraction, natural language processing and machine learning techniques to achieve the best extraction results.

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2.     Cross-Template Oblivion – the disregard of a provision that relates to something that would normally be captured under a different template. For example, conveniently ignoring Additional Events of Default that are specific to a CSA, because that’s something you’d normally capture from the Master.

3.     Gold-plated Data Dustbin – okay, you are capturing some of the data – but there’s still a high-risk multi-year cross-divisional development programme required before your key business users see much value from it, and who knows what’ll have happened by then.

4.     Fat-Tail Fatigue – effort is focussed on the high volumes of ISDA Master and CSA agreements, leaving an inconvenient gap in data across a multitude of relatively low volume agreements (non-ISDA, repo and securities lending, et cetera). Is there still an appetite to invest in all the work required to capture this information properly?

5.     Automation Fixation – any decent solution will apply technology to the problem, no doubt claiming impressive accuracy stats with esoteric references to machine learning and artificial intelligence. Face it, there’s no silver bullet. Focus on what you are getting out of the platform and the gap to your requirements, and don’t get distracted by shiny toys!

6.     Silo Mentality – it may make sense to tackle OTC Derivative collateral annexes first (just as an example), but eventually you’ll want to interrogate your data across all transaction types. Make sure that support for this isn’t a hand-waving afterthought.

7.     Exceptions are the Rule – at long last you’ve got a platform that lets you capture your collateral agreements. Except for some tricky independent amounts. And some even trickier collateral definitions. Oh, and support for concentration limits is, well, limited. Ah, and we can’t handle those complex structured finance agreements with ratings agency criteria. Sure, the support for fund agreements could do with some more work… Sounds familiar?

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